36:55

Human Stories in AI: Tommy Tang

37:27

Human Stories in AI: Simon Stochholm

03:21

Log_e Song - Official Lyric Video

35:49

Human Stories in AI: Brian Risk@devra.ai

23:43

The matrix math behind transformer neural networks, one step at a time!!!

35:31

Human Stories in AI: Fabio Urbina

27:13

Human Stories in AI: Khushi Jain

33:12

Human Stories in AI: Achal Dixit

31:13

Human Stories in AI: Rick Marks

30:01

Essential Matrix Algebra for Neural Networks, Clearly Explained!!!

32:02

Word Embedding in PyTorch + Lightning

02:30

The Golden Play Button, Clearly Explained!!!’

06:46

Another 3 lessons from my Pop!!!

36:45

Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!

36:15

Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!

15:51

Attention for Neural Networks, Clearly Explained!!!

16:50

Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!

03:27

The Ukulele: Clearly Explained!!!

16:12

Word Embedding and Word2Vec, Clearly Explained!!!

35:57

The AI Buzz, Episode #5: A new wave of AI-based products and the resurgence of personal applications

16:16

CatBoost Part 2: Building and Using Trees

08:32

CatBoost Part 1: Ordered Target Encoding

35:26

The AI Buzz, Episode #4: ChatGPT + Bing and How to start an AI company in 3 easy steps.

15:23

One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!

33:43

The AI Buzz, Episode #3: Constitutional AI, Emergent Abilities and Foundation Models

16:14

Mutual Information, Clearly Explained!!!

10:14

Cosine Similarity, Clearly Explained!!!

33:24

Long Short-Term Memory with PyTorch + Lightning

36:52

The AI Buzz, Episode #2: Big data, Reinforcement Learning and Aligning Models

38:21

The AI Buzz, Episode #1: ChatGPT, Transformers and Attention

08:20

Design Matrix Examples in R, Clearly Explained!!!

14:40

Design Matrices For Linear Models, Clearly Explained!!!

11:38

Using Linear Models for t tests and ANOVA, Clearly Explained!!!

07:43

Multiple Regression in R, Step by Step!!!

05:25

Multiple Regression, Clearly Explained!!!

05:01

Linear Regression in R, Step by Step

27:27

Linear Regression, Clearly Explained!!!

11:01

R-squared, Clearly Explained!!!

20:45

Long Short-Term Memory (LSTM), Clearly Explained

00:59

Happy Halloween (Neural Networks Are Not Scary)

06:59

Handmade Pasta, Clearly Explained!!!

20:43

Introduction to Coding Neural Networks with PyTorch and Lightning

05:28

Three more lessons from my Pop!!!

16:37

Recurrent Neural Networks (RNNs), Clearly Explained!!!

00:15

The StatQuest Illustrated Guide To Machine Learning, Theme Song!!!

23:22

The StatQuest Introduction to PyTorch

05:06

Troll 2, Clearly Explained!!!

16:02

UMAP: Mathematical Details (clearly explained!!!)

18:52

UMAP Dimension Reduction, Main Ideas!!!

09:40

Tensors for Neural Networks, Clearly Explained!!!

09:30

Clustering with DBSCAN, Clearly Explained!!!

06:28

Frank Starmer Clearly Explained (How my pop influenced StatQuest!!!)

16:35

Entropy (for data science) Clearly Explained!!!

14:00

Bayes' Theorem, Clearly Explained!!!!

10:56

Conditional Probabilities, Clearly Explained!!!

08:08

Using Bootstrapping to Calculate p-values!!!

09:27

Bootstrapping Main Ideas!!!

06:22

Ken Jee's #66DaysOfData Challenge Clearly Explained!!!

19:44

Expected Values for Continuous Variables!!!

11:10

Three (3) things to do when starting out in Data Science